An industrial control system (ICS) is an automated network of devices that make up a complex industrial process. For example, a large-scale electrical grid may contain thousands of instruments, sensors, and controls that transfer and distribute power, along with computing systems that capture data transmitted across these devices. Monitoring the ICS network for new device connections, device performance, or adversarial attacks requires sophisticated data analysis. LLNL researchers Brian Kelley, Indrasis Chakraborty, Brian Gallagher, and Dan Merl recently published a patent (pending) for a novel ML framework that discovers and predicts key data about networked devices. Keeping a data-driven eye on an ICS helps ensure its reliability and security. Kelley explains, “By training the ML model on a variety of datasets across different ICS types, such as data from large utility companies, we want it to learn about the characteristics of those systems. Then when our model is presented with data from a system it hasn’t seen before, it could recognize relevant devices and tell us about the devices’ provenance or metadata.” The pending patent enables the technology to be easily adopted in many use cases: public utilities, building systems, sensors that send and receive signal data, and more. Read more at LLNL Computing.